mlsanityData to 7 Oct 2026

Overview of the PromptCBLUE Shared Task in CHIP2023

arXiv 2312.17522 ↗First posted 29 Dec 2023cs.CLcited by 14

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Abstract (arXiv)

This paper presents an overview of the PromptCBLUE shared task (http://cips-chip.org.cn/2023/eval1) held in the CHIP-2023 Conference. This shared task reformualtes the CBLUE benchmark, and provide a good testbed for Chinese open-domain or medical-domain large language models (LLMs) in general medical natural language processing. Two different tracks are held: (a) prompt tuning track, investigating the multitask prompt tuning of LLMs, (b) probing the in-context learning capabilities of open-sourced LLMs. Many teams from both the industry and academia participated in the shared tasks, and the top teams achieved amazing test results. This paper describes the tasks, the datasets, evaluation metrics, and the top systems for both tasks. Finally, the paper summarizes the techniques and results of the evaluation of the various approaches explored by the participating teams.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). Builds on 41 of its 75 references that are in the map. First posted on arXiv 29 Dec 2023.

Builds on

41 of 75 references.

References · 41

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29 more
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  5. 2021Learning To Retrieve Prompts for In-Context Learningcited by 641
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 6; 2025: 3; 2026: 5.

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  1. 2024ALoRA: Allocating Low-Rank Adaptation for Fine-tuning Large Language Modelscited by 36
  2. 2024MiLoRA: Efficient Mixture of Low-Rank Adaptation for Large Language Models Fine-tuningcited by 19
  3. 2024A Comprehensive Survey on Evaluating Large Language Model Applications in the Medical Industrycited by 17
  4. 2025AMAS: Adaptively Determining Communication Topology for LLM-based Multi-Agent Systemcited by 15
  5. 2024Prompt engineering paradigms for medical applications: scoping review and recommendations for better practicescited by 13
  6. 2025PARA: Parameter-Efficient Fine-tuning with Prompt Aware Representation Adjustmentcited by 9
  7. 2025FT-MDT: Extracting Decision Trees from Medical Texts via a Novel Low-rank Adaptation Methodcited by 7
  8. 2026MRAG: Benchmarking Retrieval-Augmented Generation for Bio-medicinecited by 4
  9. 2024IAPT: Instruction-Aware Prompt Tuning for Large Language Modelscited by 1
  10. 2026Evaluating ChatGPT on Medical Information Extraction Tasks: Performance, Explainability and Beyondcited by 1
  11. 2026Pursuing Best Industrial Practices for Retrieval-Augmented Generation in the Medical Domaincited by 1
  12. 2026ACL: Aligned Contrastive Learning Improves BERT and Multi-exit BERT Fine-tuningcited by 1
2 more
  1. 2024Advancing Chinese biomedical text mining with community challengescited by 0
  2. 2026A Generative Model for Joint Multiple Intent Detection and Slot Fillingcited by 0